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Eliciting properties of probability distributions: the highlights

Eliciting properties of probability distributions: the highlights We investigate the problem of incentivizing an expert to truthfully reveal probabilistic information about a random event. Probabilistic information consists of one or more properties, which are any real-valued functions of the distribution, such as the mean and variance. Not all properties can be elicited truthfully. We provide a simple characterization of elicitable properties, and describe the general form of the associated payment functions that induce truthful revelation. We then consider sets of properties, and observe that all properties can be inferred from sets of elicitable properties. This suggests the concept of elicitation complexity for a property, the size of the smallest set implying the property. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM SIGecom Exchanges Association for Computing Machinery

Eliciting properties of probability distributions: the highlights

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2008 by ACM Inc.
ISSN
1551-9031
DOI
10.1145/1486877.1486886
Publisher site
See Article on Publisher Site

Abstract

We investigate the problem of incentivizing an expert to truthfully reveal probabilistic information about a random event. Probabilistic information consists of one or more properties, which are any real-valued functions of the distribution, such as the mean and variance. Not all properties can be elicited truthfully. We provide a simple characterization of elicitable properties, and describe the general form of the associated payment functions that induce truthful revelation. We then consider sets of properties, and observe that all properties can be inferred from sets of elicitable properties. This suggests the concept of elicitation complexity for a property, the size of the smallest set implying the property.

Journal

ACM SIGecom ExchangesAssociation for Computing Machinery

Published: Nov 1, 2008

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